.png){kind=icon}
返回
Robust Randomized Autoencoder and Correntropy Criterion-Based One-Class Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2020.3026393.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hierarchical neural network based one-class anomaly detection algorithms generally rely on stacked autoencoders (AEs) for feature learning. But existing AEs are not specifically designed to exploit the discriminative characteristics of target data in one-class classification (OCC), and thus may lead to poor generalization performance. In this brief, a novel randomized AE that imposes the constraint of the within-class scatter information is developed in feature learning. The correntropy criterion is applied to replace the mean square error criterion (MSE) to enhance the algorithm performance in outlier and noise rejection. The algorithm is further extended to kernel learning to improve its generalization capability. Experiments on benchmark datasets are carried out to show the effectiveness of the proposed algorithm.
Keyword:
Kernel
Feature extraction
Benchmark testing
Training
Circuits and systems
Robustness
Optimization
Within-class scatter constraint
correntropy
extreme learning machine
hierarchical network
kernel learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容